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Named-entity recognition (NER) is a preliminary step for several text extraction tasks. In this work, we try to recognize Kazakh named entities by introducing a hybrid neural network model that leverages word semantics with multidimensional features and attention mechanisms. There are two major challenges: First, Kazakh is an agglutinative and morphologically rich language that presents a challenge for NER due to data sparsity. The other is that Kazakh named entities have unclear boundaries, polysemy, and nesting. A common strategy to handle data sparsity is to apply subword segmentation. Thus, we combined the semantics of words and stems by stemming from the Kazakh morphological analysis system. Additionally, we constructed a graph structure of entities, with words, entities, and entity categories as nodes and inclusion relations as edges, and updated nodes using a gated graph neural network (GGNN) with an attention mechanism. Finally, through the conditional random field (CRF), we extracted the final results. Experimental results show that our method consistently outperforms all previous methods by 88.04% in terms of F1 scores.

With ever increasing complexity and intelligence of the modern cities, protecting key public facilities and important targets from any damage is a major challenge for the security sector. In all types of anti-terrorism prediction models, the prediction of attack behaviour is indispensable. Therefore, the attack behaviour model plays an important role in the anti-terrorism security system. This paper builds the attacker’s behaviour model, and carries out the prediction about the possible attack behaviour by the attacker model based on random strategy. According to the attack strategies, analysis and construction of the attack tree and attack graph are carried out based on the state-based stochastic model. The paper describes the security system in detail taking use of the state-based stochastic model method, so as to clarify the state distribution and the transfer relationship between the states of various security resources after threatened by attacks. At the same time, this paper applies the state-based stochastic model to establish the attacker model through the impact of attack on the security system.

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